Reliable Prediction Model Research Articles

ECMHA-PP: A Breast Cancer Prognosis Prediction Model Based on Energy-Constrained Multi-Head Self-Attention.

Breast cancer is a significant threat to women's health. Precise prognosis prediction for breast cancer can help doctors implement more rational treatment strategies. Artificial intelligence can assist doctors in decision-making and enhance predictionaccuracy. In this paper, a deep learning model ECMHA-PP (Energy Constrained Multi-Head Self-Attention based Prognosis Prediction) is proposed to predict the prognosis of breast cancer. ECMHA-PP utilizes patients' clinical data and extracts features through a cross-position mix and a channel mix multi-layer perceptron. Then, it incorporates an energy-constrained multi-head self-attention layer to improve feature extraction capability. The source code of ECMHA-PP has been hosted on GitHub and is available at https://github.com/xiaoliu166370/ECMHA-PP. To evaluate our proposed method, prognostic prediction experiments were performed on the METABRIC dataset, yielding outstanding results with an average accuracy of 93.0% and an average area under the curve of 0.974. To further validate the model's performance, we conducted tests on another independent dataset, BRCA, achieving an accuracy of 87.6%. In comparison with other widely used advanced methods, ECMHA-PP demonstrated higher comprehensive performance, making it a reliable prognostic prediction model for breast cancer. Given its robust feature extraction and predictioncapabilities.

Prediction of clinical prognosis and drug sensitivity in hepatocellular carcinoma through the combination of multiple cell death pathways.

Hepatocellular carcinoma (HCC) is the sixth most common malignant tumor, highlighting a significant need for reliable predictive models to assess clinical prognosis, disease progression, and drug sensitivity. Recent studies have highlighted the critical role of various programmed cell death pathways, including apoptosis, necroptosis, pyroptosis, ferroptosis, cuproptosis, entotic cell death, NETotic cell death, parthanatos, lysosome-dependent cell death, autophagy-dependent cell death, alkaliptosis, oxeiptosis, and disulfidptosis, in tumor development. Therefore, by investigating these pathways, we aimed to develop a predictive model for HCC prognosis and drug sensitivity. We analyzed transcriptome, single-cell transcriptome, genomic, and clinical information using data from the TCGA-LIHC, GSE14520, GSE45436, and GSE166635 datasets. Machine learning algorithms were used to establish a cell death index (CDI) with seven gene signatures, which was validated across three independent datasets, showing that high CDI correlates with poorer prognosis. Unsupervised clustering revealed three molecular subtypes of HCC with distinct biological processes. Furthermore, a nomogram integrating CDI and clinical information demonstrated good predictive performance. CDI was associated with immune checkpoint genes and tumor microenvironment components using single-cell transcriptome analysis. Drug sensitivity analysis indicated that patients with high CDI may be resistant to oxaliplatin and cisplatin but sensitive to axitinib and sorafenib. In summary, our model offers a precise prediction of clinical outcomes and drug sensitivity for patients with HCC, providing valuable insights for personalized treatment strategies.

Intracranial aneurysm instability prediction model based on 4D-Flow MRI and HR-MRI

This study aims to develop a reliable predictive model for assessing intracranial aneurysm (IA) instability by utilizing four-dimensional flow magnetic resonance imaging (4D-Flow MRI) and high-resolution MRI (HR-MRI). Initially, we curated a prospective dataset, dubbed the primary cohort, by aggregating patient data that was consecutively enrolled across two centers from November 2018 to November 2021. Unstable aneurysms were defined as those with symptoms, morphological change or ruptured during follow-up periods. We introduce a specialized ensemble learning framework, termed the Hybrid Model, which synergistically combines two heterogeneous base learning algorithms: 4D-Flow logistic regression (4D-Flow-LR) and Multi-crop Attention Branch Network (MicroAB-Net). The ability of the hybrid model to predict aneurysm instability was compared with baseline models: PHASES (population, hypertension, age, size, earlier rupture, and site) LR, ELAPSS (earlier subarachnoid hemorrhage, location, age, population, size, and shape) LR, aneurysm wall enhancement (AWE) LR, and Radiomics using the area under the curve (AUC) with Delong's test. Finally, the Hybrid Model was further validated in the validation cohort (patients enrolled between December 2021 to May 2022). In the primary cohort, 189 patients (144 women [76.2 ​%]; aged 58.90 years ​± ​10.32) with 213 IAs were included. In the validation cohort, 48 patients (35 women [72.9 ​%]; aged 55.0 years ​± ​10.77) with 53 IAs were included. The Hybrid Model achieved the highest performance both in the primary cohort (AUC ​= ​0.854) and the validation cohort (AUC ​= ​0.876). The Hybrid model provided a promising prediction of aneurysm instability.

Self-supervised learning-based multi-source spectral fusion for fruit quality evaluation: A case study in mango fruit ripeness prediction

Rapid and non-destructive techniques for fruit quality evaluation are widely concerned in modern agro-industry. Spectroscopy is one of the most commonly used techniques in this field. With the growing popularity of various spectroscopic instruments, it is indeed worthwhile to explore modeling with multi-source spectral data to achieve more accurate predictions. Nonetheless, a major challenge is acquiring enough labeled samples, as measuring fruit chemical values is laborious, expensive, and time-consuming, which hinders the development of a reliable prediction model. Therefore, this study aims to develop a model for predicting the internal chemical composition of fruits by integrating multi-source spectral fusion combined with self-supervised learning (SSL). A visible (Vis) and near-infrared (NIR) spectral dataset related to dry matter content (DMC) prediction in mango fruit is used as an example to validate the effectiveness of the proposed method. To obtain multi-source spectral data, the Vis and NIR portions are processed as two separate spectral ranges. An SSL pre-training is performed utilizing a large amount of raw unlabeled spectral data to extract general knowledge, which is subsequently migrated to a downstream task for fine-tuning. The experimental results indicate that the multi-source spectral fusion model performs better than the single-source spectral model. Moreover, SSL solves the data scarcity problem and outperforms non-pre-trained models in downstream DMC prediction tasks with less computational overhead. Remarkably, utilizing only <10 % of the total samples is sufficient to achieve a performance close to 99 % of the best results. The presented method has great potential in spectral analysis of food and agro-products.

Artificial Intelligence Prediction Model of Occurrence of Cerebral Vasospasms Based on Machine Learning.

Symptomatic cerebral vasospasms are deleterious complication of the rupture of a cerebral aneurysm and potentially lethal. The existing scales used to classify the initial presentation of a subarachnoid hemorrhage (SAH) offer a blink of the outcome and the possibility of occurrence of symptomatic cerebral vasospasms. Altogether, neither are they sufficient to predict outcome or occurrence of events reliably nor do they offer a united front. This study tests the common grading scales and factors that otherwise affect the outcome, in an artificial intelligence (AI) based algorithm to create a reliable prediction model for the occurrence of cerebral vasospasms. Applying the R environment, an easy-to-operate command line was programmed to prognosticate the occurrence of vasospasms. Eighty-seven patients with aneurysmal SAH during a 24-month period of time were included for study purposes. The holdout and cross-validation methods were used to evaluate the algorithm (65 patients constituted the validation set and 22 patients constituted the test set). The Support Vector Machines (ksvm) classification method provided a high accuracy. The medical dataset included demographic data, the Hunt and Hess scale (H&H), Fisher grade, Barrow Neurological Institute (BNI) scale, length of intervention for aneurysmal repair, etc. RESULTS: Our prediction model based on the AI algorithm demonstrated an accuracy of 61 to 86% for the event of symptomatic vasospasms. For subgroup analysis, 28.8% (n = 13) patients in the surgical cohort developed symptomatic vasospasm. Of these, 50% (n = 7) were admitted with Fisher scale grade 4, 37.5% (n = 5) with H&H 5, and 28.5% (n = 4) with BNI 5. In the endovascular cohort, vasospasms occurred in 31.8% (n = 14) patients. Of these, 69% (n = 9) patients were admitted with Fisher grade 4, 23% (n = 3) patients with H&H 5, and 7% (n = 1) patients with BNI 5. From our data, we may believe that the algorithm presented can help in identifying patients with SAH who are at "high" or "low" risk of developing symptomatic vasospasms. This risk balancing might further allow the treating physician to go for an earlier intervention trying to prevent permanent sequelae. Certainly, accuracy will improve with a higher caseload and more statistical coefficients.

Applying clustered artificial neural networks to enhance contaminant diffusion prediction in geotechnical engineering

This paper introduces a novel approach using Clustered Artificial Neural Networks (CLANN) to address the challenge of developing predictive models for multimodal dataset with extreme parameter values. The CLANN method strategically decomposes the dataset, derived from Finite Element Analysis (FEA), into clusters, each representing distinct diffusion behaviors, and applies specialized neural networks within these clusters. The CLANN model was rigorously evaluated and demonstrated superior accuracy and consistency compared to traditional methods such as the Adaptive Neuro-Fuzzy Inference System (ANFIS) and fuzzy expert systems. While these conventional models struggled to capture the full range of diffusion dynamics, particularly under extreme conditions, CLANN consistently provided predictions that closely aligned with the actual FEA data across all scenarios. The versatility of the CLANN approach extends beyond its application to soil contamination. Its ability to handle complex, multimodal datasets suggests that this methodology can be generalized to a wide range of scientific and engineering problems characterized by similar data structures. This makes CLANN not only a powerful tool for geotechnical engineers but also a promising framework for broader applications where traditional models fall short. The findings of this study pave the way for more accurate, reliable, and adaptable predictive modeling in diverse domains, enhancing our ability to manage and mitigate environmental and engineering challenges.

Predicting CO2 and H2 Solubility in Pure Water and Various Aqueous Systems: Implication for CO2–EOR, Carbon Capture and Sequestration, Natural Hydrogen Production and Underground Hydrogen Storage

The growing energy demand and the need for climate mitigation strategies have spurred interest in the application of CO2–enhanced oil recovery (CO2–EOR) and carbon capture, utilization, and storage (CCUS). Furthermore, natural hydrogen (H2) production and underground hydrogen storage (UHS) in geological media have emerged as promising technologies for cleaner energy and achieving net–zero emissions. However, selecting a suitable geological storage medium is complex, as it depends on the physicochemical and petrophysical characteristics of the host rock. Solubility is a key factor affecting the above–mentioned processes, and it is critical to understand phase distribution and estimating trapping capacities. This paper conducts a succinct review of predictive techniques and present novel simple and non–iterative predictive models for swift and reliable prediction of solubility behaviors in CO2–brine and H2–brine systems under varying conditions of pressure, temperature, and salinity (T–P–m salts), which are crucial for many geological and energy–related applications. The proposed models predict CO2 solubility in CO2 + H2O and CO2 + brine systems containing mixed salts and various single salt systems (Na+, K+, Ca2+, Mg2+, Cl−, SO42−) under typical geological conditions (273.15–523.15 K, 0–71 MPa), as well as H2 solubility in H2 + H2O and H2 + brine systems containing NaCl (273.15–630 K, 0–101 MPa). The proposed models are validated against experimental data, with average absolute errors for CO2 solubility in pure water and brine ranging between 8.19 and 8.80% and for H2 solubility in pure water and brine between 4.03 and 9.91%, respectively. These results demonstrate that the models can accurately predict solubility over a wide range of conditions while remaining computationally efficient compared to traditional models. Importantly, the proposed models can reproduce abrupt variations in phase composition during phase transitions and account for the influence of different ions on CO2 solubility. The solubility models accurately capture the salting–out (SO) characteristics of CO2 and H2 gas in various types of salt systems which are consistent with previous studies. The simplified solubility models for CO2 and H2 presented in this study offer significant advantages over conventional approaches, including computational efficiency and accuracy across a wide range of geological conditions. The explicit, derivative–continuous nature of these models eliminates the need for iterative algorithms, making them suitable for integration into large–scale multiphase flow simulations. This work contributes to the field by offering reliable tools for modeling solubility in various subsurface energy and environmental–related applications, facilitating their application in energy transition strategies aimed at reducing carbon emissions.

Research on prediction of PPV in open pit mine used on intelligent hybrid model of extreme gradient boosting

Peak particle velocity (PPV) serves as a critical metric in assessing the appropriateness of blasting design parameters. However, existing methods for accurately measuring PPV remain insufficient. To develop a robust PPV prediction model, this study integrates the Extreme Gradient Boosting (XGBoost) algorithm with four distinct optimization techniques: Runge Kutta Optimizer (RUN), Equilibrium Optimizer (EO), Gradient-Based Optimizer (GBO), and Reptile Search Algorithm (RSA). Real-time blasting data from open-pit mines are employed to predict PPV, utilizing parameters including Charge Quantity per Hole (CQH/kg), Total Charge Quantity (TCQ/kg), Distance from Bursting Point to Measuring Point (DBM/m), Drilling Depth (DP/m), Borehole Diameter (BD/mm), Spacing (S/m), Row Spacing (RS/m), Minimum Burden (MB/m), and Depth Displacement (DD/m). The predictive outcomes of the XGBoost model, optimized by various algorithms, are benchmarked against the Sadovsky empirical formula, the conventional XGBoost model, and several traditional machine learning models (Ridge, LASSO, SVM, SVR) using performance metrics including R2, RMSE, VAF, MAE, and MBE. Additionally, the Shapley Additive Explanations (SHAP) method is employed to assess the impact of various factors on PPV prediction outcomes. The findings reveal that the GBO-optimized XGBoost model surpasses the RUN, EO, and RSA-optimized XGBoost models, along with other machine learning models and traditional empirical formulas, in predicting PPV. This study further corroborates that the XGBoost model, when enhanced with various optimization algorithms, effectively manages the non-linear characteristics of multiple factors, resulting in a reliable, straightforward, and efficient PPV prediction model. Moreover, the SHAP sensitivity analysis identifies DBM, TCQ, and CQH as the primary factors influencing PPV, enabling engineers to mitigate the impact on nearby structures, equipment, and personnel through the careful adjustment of explosive quantities.

Survival models and longitudinal medical events for hospital readmission forecasting

BackgroundThe rate of 30-day all-cause hospital readmissions can affect the funding a hospital receives. An accurate and reliable readmission prediction model could save money and increase quality-of-care. Few projects have explored formulating this task as a survival prediction problem, where models can exploit a real-valued time-to-readmission target. This paper demonstrates the effectiveness of a survival-inspired readmission model, especially when paired with a longitudinal patient representation that is agnostic to disease-cohort and predictive task.MethodsWe forecast readmissions for a population-level cohort of 421,088 patients discharged in 2015 and 2016 from hospitals in Alberta, Canada. Clinical features and sequences of historical medical codes (calculated from at least four full years prior to discharge) from linked administrative sources serve as model inputs. We trained binary 30-day readmission models (XGBoost and a Deep Neural Network) and time-to-event readmission models (CoxPH and N-MTLR) with and without machine-learned medical knowledge at initialization, then compared against the popular LACE-based model using the AUROC score at 30 days (AUROC@30). Survival models are additionally evaluated using concordance, Integrated Brier, and L1-loss scores.ResultsAll models that utilize sequence features markedly out-perform even the best models trained on only clinical features. Further, a time-to-event target improves predictive performance at 30 days, given the same model inputs and architecture. N-MTLR, using solely sequence inputs and initialized with pre-learned medical knowledge, achieves an average AUROC@30 of 0.8460 over five folds with a standard deviation of 0.003. All trained models match or out-perform the LACE baseline of 0.6587±0.003.ConclusionSequences of administrative medical codes contain rich predictive information for forecasting readmissions, and embedding medical knowledge a priori using machine learning provides readmission models an advantageous foundation for training. When combined with a model that can leverage a time-to-event target, excellent performance is possible on the 30-day all-cause readmission task using only administrative data.

Abstract Su705: Predictive Model and Factors for Out-of-Hospital Cardiac Arrest in Geriatric Patients with insomnia: A Longitudinal Study

Background: Previous studies have shown that geriatric insomnia can impact the quality of life and lead to various health risks and comorbidities, which may further result in out-of-hospital cardiac arrest (OHCA). Therefore, assessing the risk of OHCA in elderly patients with insomnia is crucial. Hypothesis: We hypothesized that a predictive model incorporating demographic, clinical, and treatment adherence factors can effectively estimate the risk of OHCA among geriatric patients with insomnia. Aim: This study aimed to develop and validate a predictive model for OHCA in geriatric patients with insomnia using a large-scale population-based analysis. Method: A retrospective cohort study was conducted using data from Taiwan's Health and Welfare Data Science Center spanning from 2011 to 2020. The study included elderly patients diagnosed with insomnia who received medication. Predictors of OHCA, such as age, sex, comorbidities, medication adherence, and psychotherapy, were analyzed using multivariable logistic regression. Model performance was assessed using the area under the receiver operating characteristic curve (AUROC) with both internal and temporal validation. Results: Among 438,147 eligible elderly patients with insomnia, 7,666 experienced OHCA. Using data from 2011 to 2018 for model development, the AUROC for predicting OHCA within 7 days, 30 days, and 90 days ranged from 0.749 to 0.762 in the training set and 0.768 to 0.784 in the validation set. Key predictors included older age, male sex, prior medical resource use, hemodialysis, pre-existing comorbidities, medication possession ratio, medication changes, and recent psychotherapy (Figure 1). The model was also tested in 2019 and 2020 dataset (began of the COVID-19 in Taiwan), respectively, demonstrating consistent predictive performance (Figure 2). Specifically, the 7-day model in 2019 showed a sensitivity of 0.776, specificity of 0.632, and accuracy of 0.632, with a positive likelihood ratio (PLR) of 2.11, and negative likelihood ratio (NLR) of 0.36. In 2020, it achieved a sensitivity of 0.739, specificity of 0.661, accuracy of 0.661, PLR of 2.18, NLR of 0.40. Conclusion: The study developed a reliable predictive model for OHCA in geriatric patients with insomnia, highlighting significant predictors such as age, comorbidities, medication adherence, and recent psychotherapy. This model can aid clinicians in identifying high-risk patients and improving preventative care strategies.

Fracture Characteristics and Tensile Strength Prediction of Rock–Concrete Composite Discs Under Radial Compression

To investigate the fracture mechanism of rock–concrete (R–C) systems with an interface crack, Brazilian splitting tests were conducted, with a focus on understanding the influence of the interface crack angle on failure patterns, energy evolution, and RA/AF characteristics. The study addresses a critical issue in rock–concrete structures, particularly how crack propagation differs with varying crack angles, which has direct implications for structural integrity. The experimental results show that the failure paths in R–C disc specimens are highly dependent on the interface crack angle. For crack angles of 0°, 15°, 30°, and 45°, cracks initiate from the tips of the interface crack and propagate toward the loading ends. However, for angles of 60°, 75°, and 90°, crack initiation shifts away from the interface crack tips. The AE parameters RA (rise time/amplitude) and AF (average frequency) were used to characterize different failure patterns, while energy evolution analysis revealed that the highest percentage of energy consumption occurs at a crack angle of 45°, indicating intense microcrack activity. Moreover, a novel tensile strength prediction model, incorporating macro–micro damage interactions caused by both microcracks and macrocracks, was developed to explain the failure mechanisms in R–C specimens under radial compression. The model was validated through experimental results, demonstrating its potential for predicting failure behavior in R–C systems. This study offers insights into the fracture mechanics of R–C structures, advancing the understanding of their failure mechanisms and providing a reliable model for tensile strength prediction.

Prediction of potential antitumor components in Ganoderma lucidum: A combined approach using machine learning and molecular docking

The objective of this study is to develop a reliable predictive model for antitumour activity and to identify potential antitumour components in Ganoderma lucidum. Four machine learning models, including Random Forest, were employed to train predictive models for antitumour activity, utilising Morgan fingerprints as molecular descriptors. The most effective model was then employed to predict the chemical composition of Ganoderma lucidum, identifying the four most probable compounds for molecular docking with known TNF-α-related targets. The findings of the study indicate that a Support Vector Machine (SVM) model exhibits an accuracy, F1 score, AUC, and sensitivity of 0.7638, 0.7638, 0.8332, and 0.7621, respectively. The model demonstrated an 80 % accuracy rate in predicting the antitumour activity of 10 FDA-approved drugs. Besides, the model identified 11 components in Ganoderma lucidum, including 3-nitroanisole, with a probability of antitumour activity exceeding 0.5, indicating their potential as antitumour agents. The results of the molecular docking procedure indicated that the four most promising antitumour compounds derived from Ganoderma lucidum exhibited a favourable binding affinity with the TNF-α target. In conclusion, this study incorporated a machine learning prediction step prior to molecular docking, thereby enhancing the reliability of the latter. Furthermore, it identified previously unreported compounds in Ganoderma lucidum with potential antitumour activity, such as 3-nitroanisole.

AI4ACEIP: A Computing Tool to Identify Food Peptides with High Inhibitory Activity for ACE by Merged Molecular Representation and Rich Intrinsic Sequence Information Based on an Ensemble Learning Strategy.

Hypertension is a common chronic disorder and a major risk factor for cardiovascular diseases. Angiotensin-converting enzyme (ACE) converts angiotensin I to angiotensin II, causing vasoconstriction and raising blood pressure. Pharmacotherapy is the mainstay of traditional hypertension treatment, leading to various negative side effects. Some food-derived peptides can suppress ACE, named ACEIP with fewer undesirable effects. Therefore, it is crucial to seek strong dietary ACEIP to aid in hypertension treatment. In this article, we propose a new model called AI4ACEIP to identify ACEIP. AI4ACEIP uses a novel two-layer stacked ensemble architecture to predict ACEIP relying on integrated view features derived from sequence, large language models, and molecular-based information. The analysis of feature combinations reveals that four selected integrated feature pairs exhibit enhancing performance for identifying ACEIP. For finding meta models with strong abilities to learn information from integrated feature pairs, PowerShap, a feature selection method, is used to select 40 optimal feature and meta model combinations. Compared with seven state-of-the-art methods on the source and clear benchmark data sets, AI4ACEIP significantly outperformed by 8.47 to 20.65% and 5.49 to 14.42% for Matthew's correlation coefficient. In brief, AI4ACEIP is a reliable model for ACEIP prediction and is freely available at https://github.com/abcair/AI4ACEIP.

Challenges in developing reliable phosphorus predictive models: Unpredictable release under soil redox changes

Phosphorus (P), crucial for plant nutrition, is unevenly distributed in the Earth's crust, necessitating its supplementation in agriculture through fertilizers. However, excessive use can lead to water pollution. Our research focuses on the P adsorbing complex, investigating P release due to flooding, using 12 well- characterized soils with contrasting properties. Our research measures directly the P-adsorbing complex using adsorption/desorption isotherms. We observed that the P concentration in the solution —sufficient to prevent desorption yet low enough to avoid further sorption by the soil— decreases when the soil undergoes complete reduction (anoxia). When grouped by similarity, calcareous soils exhibit higher maximum P adsorption capacities (Xmax) under alternating reducing conditions (ARC) compared to continuous reducing conditions (CRC). In slightly acidic soils, CRC leads to a wider spread in Xmax values than ARC. For acidic, organic matter-rich soils, ARC results in the highest Xmax values (123 mmol P kg-1 soil) compared to CRC, whereas in acidic, light-textured soils, CRC shows significantly higher mean Xmax values than ARC. Nevertheless, we were unable to develop a predictive model for soil P desorption based on key intrinsic properties and climate. When an environmental or anthropogenic transformation induces anoxia, the P released does not follow a predictable pattern.

Analysis and Prediction of Path Loss for Mobile Communication Lines in Nasarawa State Using Propagation Models

The design of future-generation mobile communication systems depends critically on the path loss prediction methods and their suitability to various signal propagation regions. Even though 5G has been launched in Nigeria, its deployment still faces significant challenges especially in the suburban, and non-urban regions which still depends on 4G. Many operators are still in the process of upgrading their existing 4G networks to support network coverage and higher speed data throughput. Due to the unique mix of Nasarawa State with diverse terrains, in this study, path loss prediction for the key telecommunication networks in Nasarawa State was carried out using Signal Strength Info App. Data was collected for a period of eight weeks (August, 2nd to September, 4th 2023) focusing on 4G LTE networks operating within the 1800 MHz frequency band. Analysis of path loss using empirical models such as Hata, Egli, and COST-231 Hata models were compared with the measured path loss in different terrains of urban, sub-urban, and non-urban environments. The Root Mean Square Error (RMSE) analysis was carried out between empirical and measured path losses. Results shows that, the order of mean predicted path loss at 5km was free space model (BN&gt;CN&gt;DN.AN), Hata model (DN&gt;BN&gt;CN&gt;AN), Cos-231 Hata model (CN&gt;AN&gt;BN&gt;DN), and Egli model (AN&gt;BN&gt;DN&gt;CN). Hate model predicted the highest path loss values while Egli model predicted the lowest values. Overall, Egli model is found to be the most reliable and suitable path loss prediction model for entire Nasarawa State with RMSE value 5.99dBm, 2.90dBm and 3.14dBm for Nasarawa, Keffi and Akwanga LGA respectively. However, Cost-231 Hata model with RMSE value of 5.71dBm is suitable for path loss prediction in Karu due to its irregular terrain. The findings of this study are significant for practical applications in network planning, base station coverage, frequency allocation, and signal strength management, ensuring optimal mobile network performance across various terrains.

Microstructure evolution and high temperature air oxidation behaviour of thick Cr coatings at 1200°C

The thick Cr coated Zr-4 alloys were prepared by magnetron sputtering deposition and their microstructure evolution and high-temperature air oxidation behaviour were investigated. Oxidation tests showed that the thick Cr coatings can improve the high-temperature air oxidation resistance of Zr-4 alloy even if the α-Zr(O) layer was formed at some regions. The mechanism of high-temperature air oxidation was also discussed in detail, and these experimental results could provide data for establishing a reliable prediction model of this degradation.

Predicting Market Share Patterns using LSTM

Forecasting stock prices is a significant challenge in the financial sector, marked by its complexity and the impact of numerous variables. In this paper, we undertake a comprehensive investigation into how machine learning algorithms can be effectively applied to address this challenge. Our main goal is to clarify the methodologies and strategies that can enhance the accuracy of stock market predictions. We explore the complex process of predicting stock values and scrutinize the various machine learning algorithms that have been suggested and employed for this purpose. By critically evaluating these algorithms, we aim to provide insights into their individual strengths and weaknesses, ultimately helping the reader make an informed decision about the most appropriate algorithm for their specific forecasting requirements. Beyond algorithm selection and attribute analysis, our review also considers external factors that can significantly influence stock prices. These factors include a broad range of variables, such as economic conditions, geopolitical events, corporate news, and market sentiment. Understanding the interaction between these external elements and stock market dynamics is essential for developing more robust and reliable prediction models.

Personal Bankruptcy Prediction Using Logistic Regression Model

According to the Insolvency Department of Malaysia, as of December 2023, 233,483 Malaysians are currently involved in bankruptcy cases due to their defaults on hire purchase loans, credit card loans, personal loans, housing loans, and business loans. This is indeed a critical issue because the growing number of personal bankruptcy cases will hurt the Malaysian economy as well as society. From an individual's economic perspective, bankruptcy minimizes their chances of getting a job. Apart from that, their accounts will be frozen, they will lose control of their properties and assets, and they will not be allowed to start any business or be a part of any company's Board of Directors. Bankrupts also will be rejected from any loan application. This paper examines this problem by developing a personal bankruptcy prediction model using the logistic regression technique. This paper defines "bankrupt" as terminated members who failed to settle their loans. The sample comprised 24,546 cases with 17% settled cases and 83% terminated cases. The data included a dependent variable, i.e., bankruptcy status (Y=1(bankrupt), Y=0(non-bankrupt)), and 12 predictors. Upon completion, this paper succeeds in coming out with a reliable personal bankruptcy prediction model and significant variables of personal bankruptcy. The findings of this paper are very beneficial and significant to creditors, banks, the Malaysia Department of Insolvency, potential borrowers, members of AKPK, and society in general in raising awareness of personal bankruptcy risks and such information may help them to take preventive measures in minimizing the number of personal bankruptcy cases.

Construction of a prediction model for induction of labor based on a small sample of clinical indicator data

Because of the diversity and complexity of clinical indicators, it is difficult to establish a comprehensive and reliable prediction model for induction of labor (IOL) outcomes with existing methods. This study aims to analyze the clinical indicators related to IOL and to develop and evaluate a prediction model based on a small-sample of data. The study population consisted of a total of 90 pregnant women who underwent IOL between February 2023 and January 2024 at the Shanghai First Maternity and Infant Healthcare Hospital, and a total of 52 clinical indicators were recorded. Maximal information coefficient (MIC) was used to select features for clinical indicators to reduce the risk of overfitting caused by high-dimensional features. Then, based on the features selected by MIC, the support vector machine (SVM) model based on small samples was compared and analyzed with the fully connected neural network (FCNN) model based on large samples in deep learning, and the receiver operating characteristic (ROC) curve was given. By calculating the MIC score, the final feature dimension was reduced from 55 to 15, and the area under curve (AUC) of the SVM model was improved from 0.872 before feature selection to 0.923. Model comparison results showed that SVM had better prediction performance than FCNN. This study demonstrates that SVM successfully predicted IOL outcomes, and the MIC feature selection effectively improves the model's generalization ability, making the prediction results more stable. This study provides a reliable method for predicting the outcome of induced labor with potential clinical applications.

Probabilistic 3D Correspondence Prediction from Sparse Unsegmented Images.

The study of physiology demonstrates that the form (shape) of anatomical structures dictates their functions, and analyzing the form of anatomies plays a crucial role in clinical research. Statistical shape modeling (SSM) is a widely used tool for quantitative analysis of forms of anatomies, aiding in characterizing and identifying differences within a population of subjects. Despite its utility, the conventional SSM construction pipeline is often complex and time-consuming. Additionally, reliance on linearity assumptions further limits the model from capturing clinically relevant variations. Recent advancements in deep learning solutions enable the direct inference of SSM from unsegmented medical images, streamlining the process and improving accessibility. However, the new methods of SSM from images do not adequately account for situations where the imaging data quality is poor or where only sparse information is available. Moreover, quantifying aleatoric uncertainty, which represents inherent data variability, is crucial in deploying deep learning for clinical tasks to ensure reliable model predictions and robust decision-making, especially in challenging imaging conditions. Therefore, we propose SPI-CorrNet, a unified model that predicts 3D correspondences from sparse imaging data. It leverages a teacher network to regularize feature learning and quantifies data-dependent aleatoric uncertainty by adapting the network to predict intrinsic input variances. Experiments on the LGE MRI left atrium dataset and Abdomen CT-1K liver datasets demonstrate that our technique enhances the accuracy and robustness of sparse image-driven SSM.